CNNs: What happens from one neuron volume to the next?
Short answer: One to many
Long answer:

The point is that you use a 3D convolution in a CNN. Each kernel has the size of n*m*C (C is the numbers of feature maps) and every feature map has its own kernel(=weights) and bias.

An example:

The size of the layer 2 is: 9x9x10 (stride 1,no padding), the kernel size is 3x3x10

the dimension of the next layer would be 3x3xn (n are the number of kernels that layer 2 has)

here I found a very clear explanation:
https://www.quora.com/How-does-the-second-convolution-layer-work-on-multiple-feature-maps

I hope this helps.